This repository dives deep into to the handling of highly unstructured data and it's analysis which can help in maintaining data integrity
This repository is dedicated to the analysis and optimization of a retail data model focusing on sales transactions, user engagement, and brand metrics. It provides a detailed examination of the current data structure, identifies key flaws, suggests improvements, and includes minor data analyses to illustrate potential insights. The project aims to enhance data integrity, optimize data relationships, and improve overall data quality for more accurate and insightful analytics.
Data Model Review: Comprehensive analysis of the existing data model with a focus on sales, users, and brands. Relational Diagrams: Visual representations of the current database schema and proposed improvements to highlight relationships and identify missing links. Data Quality Assessment: Identification of data inconsistencies, missing values, and anomalies within existing tables. Proposed Solutions: Suggestions for database schema modifications to resolve identified issues and optimize data relationships. Sample Analyses: Minor analytical examples to demonstrate how data model improvements can lead to better insights. Best Practices: Guidelines for maintaining data integrity and ensuring accurate analyses in a retail context.
Data architects, database administrators, and data analysts involved in retail analytics will find this repository particularly useful. It is also intended for educational purposes, providing a real-world example of data model assessment and optimization.